






'''simple docstring'''





import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
						raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
_UpperCamelCase						:						Optional[Any]  =					False
_UpperCamelCase						:						Optional[int]  =					False
def __UpperCAmelCase   (					A					:			Namespace		)    ->   Any:
			return TrainCommand(A		)







class  snake_case__      (					UpperCamelCase):




					@staticmethod
					def 		A      (  _A  :		ArgumentParser      )    ->							Optional[int]:
								UpperCAmelCase_      :       Any          =       parser.add_parser('''train'''				, help='''CLI tool to train a model on a task.'''      )

								train_parser.add_argument(
								    '''--train_data'''				, type=_A				, required=_A				, help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''				, )
								train_parser.add_argument(
								    '''--column_label'''				, type=_A				, default=0				, help='''Column of the dataset csv file with example labels.'''      )
								train_parser.add_argument(
								    '''--column_text'''				, type=_A				, default=1				, help='''Column of the dataset csv file with example texts.'''      )
								train_parser.add_argument(
								    '''--column_id'''				, type=_A				, default=2				, help='''Column of the dataset csv file with example ids.'''      )
								train_parser.add_argument(
								    '''--skip_first_row'''				, action='''store_true'''				, help='''Skip the first row of the csv file (headers).'''      )

								train_parser.add_argument('''--validation_data'''				, type=_A				, default=''''''				, help='''path to validation dataset.'''      )
								train_parser.add_argument(
								    '''--validation_split'''				, type=_A				, default=0.1				, help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''				, )

								train_parser.add_argument('''--output'''				, type=_A				, default='''./'''				, help='''path to saved the trained model.'''      )

								train_parser.add_argument(
								    '''--task'''				, type=_A				, default='''text_classification'''				, help='''Task to train the model on.'''      )
								train_parser.add_argument(
								    '''--model'''				, type=_A				, default='''bert-base-uncased'''				, help='''Model\'s name or path to stored model.'''      )
								train_parser.add_argument('''--train_batch_size'''				, type=_A				, default=32				, help='''Batch size for training.'''      )
								train_parser.add_argument('''--valid_batch_size'''				, type=_A				, default=64				, help='''Batch size for validation.'''      )
								train_parser.add_argument('''--learning_rate'''				, type=_A				, default=3e-5				, help='''Learning rate.'''      )
								train_parser.add_argument('''--adam_epsilon'''				, type=_A				, default=1e-08				, help='''Epsilon for Adam optimizer.'''      )
								train_parser.set_defaults(func=_A      )




					def __init__(  self  :		int				, _A  :		Namespace      )    ->							List[Any]:
								UpperCAmelCase_      :       int          =       logging.get_logger('''transformers-cli/training'''      )

								UpperCAmelCase_      :       Union[str, Any]          =       '''tf''' if is_tf_available() else '''torch'''

								os.makedirs(args.output				, exist_ok=_A      )
								UpperCAmelCase_      :       Dict          =       args.output

								UpperCAmelCase_      :       Tuple          =       args.column_label
								UpperCAmelCase_      :       Any          =       args.column_text
								UpperCAmelCase_      :       Tuple          =       args.column_id

								self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
								if args.task == "text_classification":
											UpperCAmelCase_      :       Tuple          =       TextClassificationPipeline.from_pretrained(args.model      )
								elif args.task == "token_classification":
											raise NotImplementedError
								elif args.task == "question_answering":
											raise NotImplementedError

								self.logger.info(F"Loading dataset from {args.train_data}"      )
								UpperCAmelCase_      :       Dict          =       Processor.create_from_csv(
								    args.train_data				, column_label=args.column_label				, column_text=args.column_text				, column_id=args.column_id				, skip_first_row=args.skip_first_row				, )
								UpperCAmelCase_      :       str          =       None
								if args.validation_data:
											self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
											UpperCAmelCase_      :       str          =       Processor.create_from_csv(
											    args.validation_data				, column_label=args.column_label				, column_text=args.column_text				, column_id=args.column_id				, skip_first_row=args.skip_first_row				, )

								UpperCAmelCase_      :       Union[str, Any]          =       args.validation_split
								UpperCAmelCase_      :       Union[str, Any]          =       args.train_batch_size
								UpperCAmelCase_      :       Tuple          =       args.valid_batch_size
								UpperCAmelCase_      :       str          =       args.learning_rate
								UpperCAmelCase_      :       str          =       args.adam_epsilon




					def 		A      (  self  :		List[str]      )    ->							int:
								if self.framework == "tf":
											return self.run_tf()
								return self.run_torch()




					def 		A      (  self  :		Dict      )    ->							int:
								raise NotImplementedError





					def 		A      (  self  :		int      )    ->							List[str]:
								self.pipeline.fit(
								    self.train_dataset				, validation_data=self.valid_dataset				, validation_split=self.validation_split				, learning_rate=self.learning_rate				, adam_epsilon=self.adam_epsilon				, train_batch_size=self.train_batch_size				, valid_batch_size=self.valid_batch_size				, )

								# Save trained pipeline
								self.pipeline.save_pretrained(self.output      )










'''simple docstring'''





import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)

_UpperCamelCase						:						Dict  =					{
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}


class  snake_case__      (					UpperCamelCase):
					a_        =					"mvp"
					a_        =					["past_key_values"]
					a_        =					{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




					def __init__(  self  :		Any				, _A  :		List[str]=5_02_67				, _A  :		Dict=10_24				, _A  :		List[str]=12				, _A  :		Dict=40_96				, _A  :		Optional[int]=16				, _A  :		Optional[Any]=12				, _A  :		int=40_96				, _A  :		Dict=16				, _A  :		Union[str, Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		int="gelu"				, _A  :		Union[str, Any]=10_24				, _A  :		int=0.1				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.0				, _A  :		Optional[Any]=0.02				, _A  :		Optional[Any]=0.0				, _A  :		str=False				, _A  :		List[Any]=True				, _A  :		Optional[int]=1				, _A  :		Tuple=0				, _A  :		Dict=2				, _A  :		Dict=True				, _A  :		int=2				, _A  :		Tuple=2				, _A  :		List[str]=False				, _A  :		Any=1_00				, _A  :		Union[str, Any]=8_00				, **_A  :		List[str]				, )    ->							int:
								UpperCAmelCase_      :       Union[str, Any]          =       vocab_size
								UpperCAmelCase_      :       int          =       max_position_embeddings
								UpperCAmelCase_      :       Dict          =       d_model
								UpperCAmelCase_      :       Dict          =       encoder_ffn_dim
								UpperCAmelCase_      :       Optional[Any]          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       encoder_attention_heads
								UpperCAmelCase_      :       List[Any]          =       decoder_ffn_dim
								UpperCAmelCase_      :       List[Any]          =       decoder_layers
								UpperCAmelCase_      :       Dict          =       decoder_attention_heads
								UpperCAmelCase_      :       str          =       dropout
								UpperCAmelCase_      :       Optional[Any]          =       attention_dropout
								UpperCAmelCase_      :       Optional[Any]          =       activation_dropout
								UpperCAmelCase_      :       Union[str, Any]          =       activation_function
								UpperCAmelCase_      :       Union[str, Any]          =       init_std
								UpperCAmelCase_      :       Optional[int]          =       encoder_layerdrop
								UpperCAmelCase_      :       Optional[int]          =       decoder_layerdrop
								UpperCAmelCase_      :       Dict          =       classifier_dropout
								UpperCAmelCase_      :       List[Any]          =       use_cache
								UpperCAmelCase_      :       Dict          =       encoder_layers
								UpperCAmelCase_      :       Tuple          =       scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase_      :       int          =       use_prompt
								UpperCAmelCase_      :       Tuple          =       prompt_length
								UpperCAmelCase_      :       str          =       prompt_mid_dim

								super().__init__(
								    pad_token_id=_A				, bos_token_id=_A				, eos_token_id=_A				, is_encoder_decoder=_A				, decoder_start_token_id=_A				, forced_eos_token_id=_A				, **_A				, )

								if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''				, _A      ):
											UpperCAmelCase_      :       Optional[Any]          =       self.bos_token_id
											warnings.warn(
											    F"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''      )


